1Department of Pharmaceutical Chemistry, University of California, San Francisco, CA, USA.

Abstract

MOTIVATION:

Clustering protein sequence data into functionally specific families is a difficult but important problem in biological research. One useful approach for tackling this problem involves representing the sequence dataset as a protein similarity network, and afterwards clustering the network using advanced graph analysis techniques. Although a multitude of such network clustering algorithms have been developed over the past few years, comparing algorithms is often difficult because performance is affected by the specifics of network construction. We investigate an important aspect of network construction used in analyzing protein superfamilies and present a heuristic approach for improving the performance of several algorithms.

RESULTS:

We analyzed how the performance of network clustering algorithms relates to thresholding the network prior to clustering. Our results, over four different datasets, show how for each input dataset there exists an optimal threshold range over which an algorithm generates its most accurate clustering output. Our results further show how the optimal threshold range correlates with the shape of the edge weight distribution for the input similarity network. We used this correlation to develop an automated threshold selection heuristic in order to most optimally filter a similarity network prior to clustering. This heuristic allows researchers to process their protein datasets with runtime efficient network clustering algorithms without sacrificing the clustering accuracy of the final results.

AVAILABILITY:

Python code for implementing the automated threshold selection heuristic, together with the datasets used in our analysis, are available at http://www.rbvi.ucsf.edu/Research/cytoscape/threshold_scripts.zip.

Visualizing MCL Clusters for the SLC Superfamily. Each set of clustering results has been visualized in Cytoscape using the Force-directed layout algorithm. Each node represents a protein, colored by the currently best available family assignments. Edges between nodes that are not in the same cluster have been removed from the similarity network prior to visualization. The unthresholded clustering results are shown in (A) and the thresholded clustering results are shown in (B). The same thresholded network is shown unclustered in Supplementary Figure 4b. The mapping of node colors to family assignments is shown in Supplementary Figure 5a.

Visualizing MCL Clusters for the Kinase Superfamily. Each set of clustering results has been visualized in Cytoscape using the Force-directed layout algorithm. Each node represents a protein, colored by the currently best available family assignments. Edges between nodes that are not in the same cluster have been removed from the similarity network prior to visualization. The unthresholded clustering results are shown in (A) and the thresholded clustering results are shown in (B). The same thresholded network is shown as unclustered in Supplementary Figure 4d. The mapping of node colors to family assignments is shown in Supplementary Figure 5b.